factor

README

Factor

A fast, lightweight AI agent that lives on your machine — with hands on your desktop, a voice on the phone, and a real memory.

Factor is a single static Go binary. Talk to it in the terminal, on Telegram, over the phone, or out loud to the machine itself — same agent, same tools, same memory every time. It drives a real browser, works your desktop, runs long tasks in the background, and calls or texts you when one lands. Long-term memory is smrti: it consolidates and prioritizes rather than logging, so past failures become constraints it doesn't repeat.

Highlights

🧠 Memory as the soul	Salience-ranked recall every turn, consolidation that decays and prunes, plus remember / recall / forget / reflect tools
⚡ Never keeps you waiting	Says what it's about to do while the tools run; long work becomes a background job that messages you when it lands
🎯 Mid-turn steering	A second message during a live turn is injected between tool iterations, not queued
❓ Asks when only you know	ask_user puts the question where you are: the chat the turn came from, the terminal you're already in, or a dialog on your desktop — and times out rather than hanging the turn when you're away
🔁 Provider failover	OpenAI-compatible (OpenRouter, Ollama, LM Studio, Groq, llama.cpp, …) and native Anthropic, with per-candidate cooldowns and overflow compaction
💸 Counts what it spends	Every call priced and billed to its session — status bar, tray, usage tool — with per-session and global caps; cache reads and writes priced at their own rates, not as fresh input
♻️ Caches the prompt prefix	The same assembly order every turn, with cache breakpoints where two turns first differ, so the provider reuses the longest prefix it can — and a turn twenty tool calls deep stops reprocessing its own history
🧭 Reasoning, dialect-translated	One provider.reasoning setting becomes reasoning, reasoning_effort, or a thinking budget
☎️ Answers the phone	A real number to call, or it calls and texts you — barge-in, voicemail detection, optional fully local speech (Phone)
🎙️ Listens in the room	Mic in, speakers out, barge-in, optional wake word, push-to-talk via factor talk (PC voice)
👥 Tells voices apart	A recording is read voice by voice, so each person holds their own conversation — and company in the room moves the answer to a memory space it can hear (PC voice)
🖐️ Hands on your desktop	Windows, screenshots, mouse, keyboard, clipboard, notifications on X11/Wayland/macOS/Windows — plus grid vision (Desktop)
🌐 A real browser, not just fetch	CDP tools attach to your running Chrome/Chromium/Brave or launch a managed one (Browser)
🧩 Extensible everything	Channel connectors, Go tools, runtime-mounted MCP servers, markdown skills (Extending)
📊 Watches its own numbers	Every turn leaves a local trace — models, tools, timings, cache and cost — and control bands measure it against a rolling baseline, so the heartbeat wakes a model only once a number has drifted
📓 Learns from its own work	A turn that took four or more tool calls becomes a skill it writes for itself once the session goes quiet (Extending)
🔧 Self-managing	Edits its own config, installs packages, upgrades and restarts itself, schedules cron jobs and one-off reminders, runs HEARTBEAT.md checks that cost nothing when idle
🛡️ Safety rails	Workspace-restricted files, exec deny-patterns, sender allowlists, scrubbed secrets — rails, not a sandbox (Security)

How it works

Bus + bounded workers, mid-turn steering, narrow pluggable seams, CGO-free portability — PicoClaw's architecture in a codebase that runs happily on an old Puppy Linux box.

Get started

go install github.com/cyqlelabs/factor/cmd/factor@latest
# or grab a release binary; linux-amd64 targets GOAMD64=v1 (no SSE4.2 needed)

factor init      # interactive setup wizard

The wizard verifies every step live — a real provider completion, the endpoint's model list, Telegram's getMe, carrier and voice credentials, an actual page load — and installs what's missing: smrti, a browser, your desktop backend's helpers. It probes the machine's display rather than this shell, so setup over ssh targets the right desktop, and it can add a login entry (systemd or XDG autostart, launchd, the Windows Run key). factor init -y takes the defaults; --no-install installs nothing.

export FACTOR_PROVIDER_API_KEY=sk-or-...   # OpenRouter by default
factor                                     # interactive chat
factor -m "what's on my disk?"             # one-shot
factor gateway                             # daemon: Telegram, phone, PC voice, cron, heartbeat, jobs
factor gateway -d                          # the same, detached (~/.factor/gateway.log)
factor talk                                # push-to-talk: arm the PC voice microphone
factor status                              # daemon / provider / memory / phone / voice / desktop health
factor upgrade                             # replace this binary with the newest release
factor -p 127.0.0.1:8080                   # route HTTP through a proxy and watch every call

-p routes Factor's HTTP through any proxy — mitmproxy, Burp, ZAP, SOCKS5 — so you can read the prompts, tool schemas, replies and token counts it actually sends. On a desktop, the running gateway also puts a status icon in the system tray: version, uptime, memory health, connected channels, and a clean quit.

Proxy details, the memory sidecar, and how upgrades land

Loopback stays direct and child processes inherit the proxy setting, so smrti's calls show up but the local sidecars aren't caught. --proxy-ca trusts an intercepting proxy's CA, probed once at startup. The browser isn't routed; it has its own trust store. The tray is absent on a headless box and on macOS, whose tray would cost the build its CGO-free binaries.

Factor supervises the smrti sidecar, restarts it with backoff, and degrades gracefully (empty recalls, dropped writes) when it's down. Point memory.mode: "external" + memory.url at a shared smrti if you run one.

factor upgrade downloads the release for this machine, verifies it against the published SHA256SUMS, and swaps the binary in place (--check only reports). A running gateway restarts into it once the turn in flight is answered, keeping its pid so systemd never sees it stop. Factor checks daily and tells you, never installing unasked.

The same command brings smrti up to date however it runs here: a container is recreated on the newly published image, and a uv, pipx, pip or venv install is upgraded by the installer that made it, then restarted into. Both wait for the memory graph to go quiet first, so nothing in flight is lost. An engine on another machine is left to whoever runs it.

Configuration

~/.factor/config.json — every key optional, defaults work. FACTOR_* env overrides: FACTOR_PROVIDER_API_KEY, FACTOR_PROVIDER_MODEL, FACTOR_MEMORY_MODE, …

A running gateway watches the file. Save an edit, by hand or through the agent's own config_set, and it reloads within seconds — after the turn in flight is answered, and without restarting the sidecars — then names the changed sections in the chat it reports back to. A save that doesn't parse is warned about and retried, never applied.

Annotated example

{
  "log_level": "info",                       // debug | info | warn | error
  "agent": {
    "context_window_tokens": 0,              // 0 = ask the model catalog; a value only ever shrinks its answer
    "max_tool_iterations": 20,
    "summarize_at_percent": 75,              // how full the window gets before compaction
    "keep_recent_messages": 8,               // what survives it
    "learn_skills": true,                    // distill a finished multi-tool turn into a skill
    "version_workspace": false               // keep a local git history of the workspace, so an edit can be undone
  },
  "provider": {
    "type": "openrouter",                    // openrouter|openai|groq|ollama|lmstudio|llamacpp|anthropic|custom
    "api_key": "sk-or-...",
    "model": "google/gemini-3.1-pro-preview",
    "reasoning": { "effort": "xhigh" },      // or {"max_tokens": 12000}; "none" turns it off
    "fallbacks": [{ "type": "ollama", "model": "qwen3:8b" }],
    "utility": [{ "type": "ollama", "model": "qwen3:8b" }]  // cheaper chain for compaction summaries and skill verdicts; omit = the main one
  },
  "memory": {
    "mode": "sidecar",                       // sidecar | external | off
    "auto_install": true,                    // install smrti when it is missing
    "personality": "balanced",               // analytical | curious | empathetic | maverick | deterministic
    "space": "main",                         // where conversations are remembered
    "space_strategy": "origin",              // origin: cron and job turns use system_space | single: one space for all
    "system_space": "system",
    "shared_space": "shared"                 // what a turn other people can hear reads and writes
  },
  "channels": {
    "telegram": { "token": "123:ABC", "allow_from": ["your-telegram-id"] },
    "phone": {                                 // optional; absent = nothing runs
      "user_number": "+15550001111",           // you: the only one who may call in
      "phone_number": "+15550002222",          // the number you bought
      "carrier": "twilio",                     // twilio | telnyx
      "twilio_account_sid": "AC...",           // twilio: these two
      "twilio_auth_token": "...",
      // telnyx instead: "telnyx_api_key", "telnyx_connection_id", "telnyx_public_key"
      "elevenlabs_api_key": "...",
      "stt_api_key": "...",                    // Deepgram
      "language": "en",
      "stt": { "provider": "deepgram" },       // deepgram | whisper | local-openai
      "tts": { "provider": "elevenlabs" },     // elevenlabs | local-openai
      "proactive": "sms",                      // sms | call | off
      "max_call_minutes": 15
    },
    "voice": {                                 // PC voice: this machine's mic and speakers
      "activation": "wake-word",               // always | wake-word | push-to-talk
      "wake_word": "factor",
      "language": "en",
      "stt": { "provider": "deepgram" },       // deepgram | whisper | local-openai
      "stt_api_key": "...",                    // Deepgram
      "tts": { "provider": "elevenlabs" },     // elevenlabs | local-openai
      "elevenlabs_api_key": "...",
      "speaker_id": false,                     // tell the room apart; needs a local speech tier
      "speaker_threshold": 0.35,               // similarity below which a voice is nobody enrolled
      "unknown_speaker": "anonymous",          // anonymous | enroll
      "room_isolation": null,                  // null = on wherever speaker_id is
      "room_timeout_minutes": 30,              // how long a voice counts as still in the room
      "output_volume": 100,                    // 1–100; lower it when the speakers reach the mic
      "ignored_chime": true                    // a soft tone when it heard you and did not take it
    }
  },
  "mcp": {
    "servers": { "github": { "command": "github-mcp-server", "args": ["stdio"] } }
  },
  "tools": { "disabled": [], "restrict_to_workspace": true },
  "desktop": { "enabled": null },            // null = on when a display exists
  "browser": {
    "enabled": true,
    "command": "",                           // "" = find one; init records what it installed
    "headless": false,
    "fast_path": false                       // opt in to the lightweight read-only engine
  },
  "heartbeat": { "enabled": true, "interval_minutes": 30 },
  "trace": {
    "enabled": true,                         // one JSON line per turn in ~/.factor/traces
    "record_args": false,                    // the shape of a turn, not what was said to the tools
    "keep_days": 14
  },
  "upgrade": { "check": true, "check_interval_hours": 24 },  // report new releases; never install one unasked
  "cost": {
    "track": true,                           // price every call; models served locally cost nothing
    "budget": {
      "session_usd": 0,                      // 0 = no cap, on both scopes
      "global_usd": 0,
      "period": "month"                      // what "global" counts: day | month | total
    },
    "prices": {}                             // USD per million tokens, for models the catalog does not list
  }
}

Spend is priced from the model catalog cached in ~/.factor/pricing.json and totalled in ~/.factor/usage.json, per session and overall. Locally served models are free; models the catalog doesn't list are counted in tokens rather than guessed at. Caps are checked before the call, and the turn answers with a line saying what stopped. Ask for usage to see the breakdown.

The workspace (~/.factor/workspace) is the agent's home. The persona is built into the binary, so an upgrade improves it everywhere at once; SOUL.md layers yours on top, USER.md holds what Factor should always know about you, AGENT.md tunes how it works, HEARTBEAT.md lists proactive tasks, and instructions/, skills/, sessions/, cron/ do what they say.

Browser

Factor drives a real browser over DevTools: it attaches to your running Chrome/Chromium/Brave, or launches a managed instance that stays visible so you can watch it work. A machine with none gets Helium installed under ~/.factor/engine — ungoogled-chromium with uBlock Origin bundled, from a portable tarball that needs no package manager and no root.

Reading a page and driving a page cost wildly different amounts, so you can add a second engine for the cheap half:

Engine	Tools	Renders	Good for
Chromium — Helium, or the browser you already run	browser_navigate · _read · _scroll · _click · _fill · _keys · _upload · _tabs · _screenshot · _eval · _back	yes	anything interactive
Lightpanda — opt-in, browser.fast_path	browser_fetch — title, text, links	never	reading a page for a fraction of the memory

Browse as yourself. Start your everyday browser with --remote-debugging-port=9222 (or point browser.attach_url at it) and Factor uses that session instead of launching one — your logins, your cart, your cookies — so sites that turn away a fresh automated profile serve it normally.

browser_read puts main content first and site furniture last, says how much it withheld, and takes filter/limit; browser_scroll reaches what only loads on the way down. Lightpanda keeps no session and can't click, fill or screenshot, so it supplements the real browser rather than replacing it; its builds need glibc 2.34, which the wizard checks before the 150 MB download.

Desktop

Factor works the graphical session through the desktop's own helper programs (xdotool, wmctrl, scrot on X11; grim/wtype on Wayland; osascript on macOS; PowerShell on Windows) — no CGO bindings, and nothing registers on a headless box.

On top of the window/mouse/keyboard/clipboard tools sits grid vision, a two-pass pointing loop for vision-capable models:

1. screen_view captures the screen under a battleship coordinate grid — columns A, B, C…, rows 1, 2, 3…. The model names the cell it sees ("the icon is in D4") instead of guessing pixel coordinates, which vision models are bad at.
2. screen_zoom cell=D4 magnifies that cell — or any pixel region, e.g. a window's geometry from window_list — under a finer sub-grid, down to ~10px precision.
3. mouse action=click cell=B3 clicks the cell's center, resolved back to native screen pixels on either view.

Pure Go image math — no OpenCV, no OCR, no helpers beyond the screenshot program. Frames are capped at 1568px on the longest side (clicks still land at native resolution), only the two newest stay in context, and image bytes never touch session history. Non-vision models can disable both vision tools via tools.disabled.

Phone calls and SMS

Give Factor a phone number and it picks up: you talk, it answers out loud, with the same memory, tools, and history it has everywhere else. It can also call or text you — a finished job, a cron result, or because you asked it to ring someone.

factor init        # the Channels step walks through the carrier and the speech tier
factor gateway     # brings the line up
factor status      # number, speech tier, voice-shell health

Carrier setup has a page of its own: Twilio · Telnyx.

The voice shell is Patter, installed on demand and supervised like the smrti sidecar. It owns the carrier's media stream, turn-taking, barge-in, voice activity detection, answering-machine detection and transcoding; Factor is its brain, over an endpoint that never leaves 127.0.0.1.

Speech tiers — the one decision with real trade-offs, asked by the wizard:

Tier	Speech-to-text	Text-to-speech	Extra RAM	When
1 · cloud (default)	Deepgram nova-3	ElevenLabs flash v2.5 (µ-law 8 kHz, no transcode)	~150–300 MB	any machine; lowest latency, least to go wrong
2 · local STT	Parakeet / Whisper	ElevenLabs	+0.5–2 GB	transcription stays home
3 · local TTS	Deepgram	Piper	+0.3 GB	Piper's ~100 ms render beats the cloud, on any CPU
4 · fully local	Parakeet / Whisper	Piper	+1–2 GB	no audio leaves the machine, no per-minute audio cost

A tier picks who serves each half of the pipeline; everything else is the same call:

Pick a local tier and Factor installs it: engines in their own virtualenv, your language's models on disk before setup finishes, nothing to start by hand.

Roughly $0.04–0.06 per talk-minute on tier 1 plus your model's tokens, and about 1.3¢ per SMS segment. Simple questions land in 1.5–3 s. The reply arrives whole, but a tool-using turn still speaks: the line Factor says on its way to the answer is streamed into the live call while the tools run.

Which languages and voices you get, and which transcriber

Transcription covers Whisper's ~99 languages, with Parakeet serving its 25 at higher accuracy where the machine allows. Voices come from Piper's catalogue of 49, resolved from your language setting with the exact locale winning where it exists (es-MX gets a Mexican voice, not a Castilian one). You can also pick the voice by name: the wizard lists your ElevenLabs voices on the cloud tier and the catalogue's on the local one, and a voice named in speech_server.piper_voice is downloaded on the next start.

speech_server.speech_speed paces it: 0.9 speaks a tenth slower, 1.1 a tenth faster. Piper stretches the phonemes rather than the audio, so the voice keeps its pitch — past about 1.2 either way it stops sounding like a person.

Whisper decodes a fixed 30-second window however little audio it gets, and the phone pipeline feeds it about a second at a time — so its cost is per chunk, not per second of speech. Measured on this design: small takes ~2.4 s per 1 s chunk on a CPU and falls behind while you talk; base keeps up at ~0.9 s but mishears more. That used to be the CPU's ceiling.

Parakeet TDT 0.6B v3 breaks the trade: a transducer's cost scales with the audio it is handed, not a fixed window, and its accuracy benchmarks at Whisper large-v3 level. So the installer picks:

Machine	Transcriber
GPU	Whisper large-v3-turbo — every language, large-class accuracy
CPU, ≥4 GB RAM, one of Parakeet's 25 languages	Parakeet TDT int8 (~1 GB resident)
CPU otherwise	Whisper base/tiny, as before

speech_server.stt_engine (parakeet / whisper) or speech_server.whisper_model override the choice. On a machine that lands on base, tier 3 — Piper's ~100 ms render locally, transcription in the cloud — is still the better trade, and the wizard says so.

Bring your own speech server

Point stt.base_url / tts.base_url at anything OpenAI-compatible — Speaches or your own — and Factor uses it as-is. It probes at startup either way and falls back to the cloud tier if the server isn't answering; set local_audio_fallback: false to have the channel report itself down instead. Silero voice-activity detection runs locally in every tier.

Getting the line up, and the guardrails on it

Buy a number at Twilio or Telnyx, then run factor init. The wizard asks which one, takes the credentials, and verifies them live before writing anything.

	Twilio	Telnyx
Credentials	account SID + auth token	API key + connection id + public key
Setup at the carrier	buy a number	buy a number, create a Call Control Application
Cost	the baseline above	lower per minute and per text
Step by step	docs/phone-twilio.md	docs/phone-telnyx.md

The carrier is pointed at the shell on every start — Patter does it for Twilio, Factor for Telnyx — so a rotating tunnel keeps working with nothing to click.

The rails are closed by default, because a number is dialable by anyone and every minute costs money: only user_number may call in, only user_number may be dialed, calls are cut off at max_call_minutes, and transfer is off. Two tools appear once the channel is configured — phone_sms sends a text and phone_call dials, reporting the outcome with a transcript tail into the conversation that asked.

Your carrier's page has the rest: the allowlist knobs that widen those rails, how to move off the default Cloudflare quick tunnel — fine for a first call, wrong for daily use — and what to check when the line does not come up.

PC voice: mic and speakers

The same conversation without a phone bill: Factor listens on the machine's own microphone and answers through its speakers. The mic opens whenever Factor runs — in factor (the terminal chat keeps working alongside) and in factor gateway alike.

factor init        # the Channels step sets up the mic, the speech tier, and the activation
factor             # or factor gateway — either one listens
factor talk        # push-to-talk: arm the microphone from any terminal
factor status      # tier, activation, helpers, and whether anything is listening

activation	It answers
always	every utterance — best alone in a quiet room
wake-word	utterances that open with the wake word, plus a short window after each reply so follow-ups don't need it (the wizard preselects this)
push-to-talk	nothing until factor talk arms the microphone

factor talk works in every mode: it rescues a misfired wake word and cuts off whatever is playing, and /talk does the same inside the chat. Talk over a reply and it stops mid-word, cancelling the turn behind it. The speech tiers are the phone's, chosen the same way, with the local server on its own port so both channels can keep speech at home.

It hears people, not clips. Two people talking to each other leave gaps far shorter than the silence that closes a recording, so their whole exchange arrives as one clip — and a single embedding of that is a blend that hands both of them one name. With speaker_id on, the speech server separates the voices first and reads each one alone. The owner keeps the main conversation, a recognized guest gets a session of their own and is named to the agent as the person speaking, and voice_speakers turns a profile born speaker-2 into Roxana.

And it knows who is listening. That, not who asked, is the question confidentiality turns on. A second voice in a recording makes the room shared — at most one of them is the owner, so the rest are company — which holds before anyone is enrolled and even when nobody could be named, so long as the voice holds a second of speech of its own.

Room	Session	Recalls from	Remembers into
private	voice:local, or the guest's own	space + shared_space	space
shared	voice:local:room — everyone in one thread	shared_space	shared_space

Three seconds of a voice that is not yours, added up over a couple of minutes, declares company — a cough, a shout or the "Gracias." Whisper hears in noise never gets there, and a guest does within their first few remarks; room_timeout_minutes of silence or the room tool takes it back. The asymmetry is deliberate: a room wrongly called shared costs you a coy answer, one wrongly called private says something private to a guest.

Microphone, meter and barge-in

Audio rides the sound system's own helpers — parec/paplay, pw-record/pw-play, arecord/aplay, sox's rec/play on macOS, sox's waveaudio driver on Windows — installed by the wizard, which also asks which microphone and proves it live: you make a noise, it measures, and a silent source is called out on the spot. The chat's status bar carries a live meter — mic ▂▄▁ moves with the room and turns green on speech, ♪ lights cyan while Factor talks, a dead source shows mic ✗.

Voice activity detection is pure Go: adaptive noise floor, a pre-roll so the first syllable survives, and a higher bar while the agent speaks so the speakers can't barge in on themselves. Speakers loud enough get past that bar anyway, so what got through is matched against the words Factor just sent them: its own voice is dropped instead of answered, or stripped off the front of what you actually said. output_volume turns the reply down in rooms where the speakers overpower the microphone.

A sentence the wake word never reached gets a soft two-note chime, so a misfire sounds different from a machine that never heard you. It is a tone and not a voice, so talking across it is not an interruption and still needs the wake word; it waits fifteen seconds between chimes, because a conversation held in front of the machine is turned away sentence by sentence. ignored_chime turns it off.

How a voice becomes a name

Each voice is matched against the profiles in ~/.factor/voice-speakers.json, which needs a local speech tier — that is what computes the embeddings. The turn belongs to whoever opened the recording, since the wake word was at its front; anyone who joined halfway through is the room, not the asker. unknown_speaker decides what a new voice gets: a profile on the spot, or the main conversation, unnamed.

Three bars rise with the stakes: one second of speech to put a name to a voice, two to fold it into that profile, three to create one, because a spurious profile never goes away and competes for every match after it. Every decision lands in the log with the similarity behind it, so a turn answered as the wrong person is readable rather than a guess.

The room is read from every utterance the mic resolves, including the ones the wake word turned away — someone talking to you is still someone in the room. Sound only reports people who make it, so the room tool is how you mention someone who came in quietly or left. Either flip is spoken before the answer that depends on it, and the room outlives the process, aged against the wall clock rather than uptime, so a 9pm upgrade doesn't bring Factor back up private while the guest is still on the sofa.

Replies you can listen to

A spoken turn is told it's being heard rather than read, so replies come out sayable — no markdown, no bullet lists, no spelled-out URLs. voice_write sends anything long or written to your terminal instead, or to the chat you last used when Factor runs as a daemon.

The local tier keeps to itself. Factor sets ORT_DISABLE_TELEMETRY=1 in the speech process's environment before it starts, because onnxruntime otherwise uploads your OS build, CPU, memory and a persistent device id as it initializes, and its own disable_telemetry_events() runs too late to stop it (onnxruntime#25573).

Extending Factor

Seam	What it takes
Connector	One package: channel.Register(name, factory) in init(), with its own config section
Tool	Four methods — Name, Description, Parameters, Execute — and one registry.Register(t) line
MCP server	mcp_add (or the mcp.servers config section) mounts its tools at runtime — no Go required
Skill	Drop workspace/skills/<name>/SKILL.md — catalog in prompt, full text on demand, skill_find searches the public registry (skills.sh) and skill_install takes its slug, a git URL, or a directory

It also writes its own. Factor remembers a turn that took four or more tool calls; once that session sits quiet for ten minutes, it spends one metered call asking whether the trajectory holds a workflow worth keeping. Most of the time the answer is SKIP. A LEARN lands as a skill in the same catalog, marked learned: true, and the next turn lists it like any other. Induction rewrites its own output and never a skill you wrote or installed — skill_write drops the marker, which is how a learned skill graduates out of its reach. The learned library holds 40, so past that induction has to improve an entry rather than mint a near-duplicate. agent.learn_skills: false, or FACTOR_LEARN_SKILLS=0, turns it off.

Wiring in a connector

func init() {
    channel.Register("mychat", func(raw json.RawMessage, b *bus.MessageBus) (channel.Channel, error) {
        var cfg MyConfig
        _ = json.Unmarshal(raw, &cfg)          // your own config section
        return New(cfg, b), nil                // implement Name/Start/Stop/Send/MaxMessageLength
    })
}

Security model

Factor is a personal agent, not a multi-tenant service. The guardrails (workspace restriction, exec deny-patterns, allowlists, secret redaction) protect against accidents and casual prompt-injection — they are not a security boundary. Run it under your own account for yourself; set channels.telegram.allow_from; keep restrict_to_workspace on unless you know why you're turning it off. The phone channel is the one place the default is closed rather than open — a number anyone can dial, with a bill attached to every minute, earns stricter rails.

Development

make check        # gofmt + vet + race tests + coverage gate (≥90%, what CI runs)
make lint         # golangci-lint (its own CI job, so check alone is not the gate)
make hooks        # point git at .githooks: every commit lints first
make build        # local binary
make build-all    # release cross-compile (incl. GOAMD64=v1 for old x86-64)
make build-tiny   # -tags nobrowser: smallest binary

The suite runs against fakes — scripted providers, a fake smrti sidecar and a fake voice shell (both spawned by re-execing the test binary), a fake Telegram API, a fake carrier, a scripted microphone and speaker, a fake MCP server over real stdio JSON-RPC, a scripted desktop — plus live headless-Chrome and desktop round-trip tests that auto-skip where the machine can't host them.

Decisions worth the argument, and the alternatives they beat, live in docs/decisions/.

License

MIT © CyqleLabs